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Databricks Databricks-Machine-Learning-Professional 考試大綱：

主題簡介
主題 1
  • Test whether the updated model performs better on the more recent data
  • Identify when retraining and deploying an updated model is a probable solution to drift
主題 2
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
主題 3
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
主題 4
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
主題 5
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
主題 6
  • Identify that data can arrive out-of-order with structured streaming
  • Identify how model serving uses one all-purpose cluster for a model deployment
主題 7
  • Identify a use case for HTTP webhooks and where the Webhook URL needs to come
  • Identify advantages of using Job clusters over all-purpose clusters
主題 8
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
主題 9
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
主題 10
  • Create, overwrite, merge, and read Feature Store tables in machine learning workflows
  • View Delta table history and load a previous version of a Delta table

>> Databricks-Machine-Learning-Professional熱門認證 <<

Databricks-Machine-Learning-Professional考題資訊，Databricks-Machine-Learning-Professional參考資料

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最新的 ML Data Scientist Databricks-Machine-Learning-Professional 免費考試真題 (Q128-Q133):

問題 #128
Which stage in the MLflow Model Registry is typically used for models currently serving production traffic?

答案：B

解題說明：
In MLflow Model Registry stages:
Staging -> testing before release
Production -> serving real users
Archived -> retired models


問題 #129
A Machine Learning Engineer is tasked with building an automated daily pipeline that updates a customer_features table in Unity Catalog. They have implemented a function, compute_customer_features, that returns a DataFrame with a unique customer_id as the primary key and want to ensure the latest feature values are merged into the table each day. Which code snippet implements this requirement?

答案：D

解題說明：
Using write_table with mode set to merge updates existing rows and inserts new ones based on the table's primary key. This ensures that the latest feature values for each customer_id are merged into the existing feature table each day without overwriting the entire table, which is the correct and scalable approach for maintaining up-to-date customer features in Unity Catalog.


問題 #130
A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:

Which lines of code can be used to fill in the blank to successfully complete the code block to accomplish the task?

答案：B

解題說明：
When logging a custom Python model with mlflow.pyfunc.log_model(), the artifacts parameter is used to specify a dictionary (my_dict) of file names and their paths that the model may need to access during loading or inference. These files are stored as model artifacts and become available to the model through the load_context method.


問題 #131
A Machine Learning Engineer wants to implement MLOps. They currently have three environments: dev, stage, and prod. They have many private PyPI packages that they use to train their models, and they are concerned that their environments will not be consistent between dev, stage, and prod. The engineer needs to follow enterprise scaling and CI/CD best practices to ensure that their environments are consistent. Which approach will do this?

答案：C

解題說明：
Defining dependencies at the job or task level using Databricks Asset Bundles or Terraform aligns with enterprise MLOps and CI/CD best practices. This approach makes environment configuration declarative, version-controlled, and reproducible across dev, stage, and prod, ensuring consistent use of private PyPI packages without relying on mutable clusters or manual installation steps.


問題 #132
Why is Apache Spark useful for machine learning training?

答案：A

解題說明：
Spark processes large distributed datasets efficiently.


問題 #133
......

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